Workshop

Deploy the LLM Application

Deploy the LLM Application #

Use the following command to deploy this application to the OpenShift cluster:

cd ~/workshop/cisco-ai-pods
oc apply -f ./llm-app/k8s-manifest.yaml

Note: to build a Docker image for this Python application, we executed the following commands:

bash Copy

cd workshop/cisco-ai-pods/llm-app
docker build --platform linux/amd64 -t ghcr.io/splunk/cisco-ai-pod-workshop-app:1.0 .
docker push ghcr.io/splunk/cisco-ai-pod-workshop-app:1.0

Test the LLM Application #

Let’s ensure the application is working as expected.

Start a pod that has access to the curl command:

oc run curl --rm -it --image=curlimages/curl:latest \
  --overrides='{
    "spec": {
      "containers": [{
        "name": "curl",
        "image": "curlimages/curl:latest",
        "stdin": true,
        "tty": true,
        "command": ["sh"],
        "resources": {
          "limits": {
            "cpu": "50m",
            "memory": "100Mi"
          },
          "requests": {
            "cpu": "50m",
            "memory": "100Mi"
          }
        }
      }]
    }
  }'

Then run the following command to send a question to the LLM:

curl -X "POST" \
 'http://llm-app:8080/askquestion' \
  -H 'Accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "question": "How much memory does the NVIDIA H200 have?"
  }'

The NVIDIA H200 has 141GB of HBM3e memory, which is twice the capacity of the NVIDIA H100 Tensor Core GPU with 1.4X more memory bandwidth.